[SPARK-28473][DOC] Stylistic consistency of build command in README

R/README.md

@@ -20,7 +20,7 @@ export R_HOME=/home/username/R
 Build Spark with [Maven](https://spark.apache.org/docs/latest/building-spark.html#buildmvn) and include the `-Psparkr` profile to build the R package. For example to use the default Hadoop versions you can run
 
 ```bash
-build/mvn -DskipTests -Psparkr package
+./build/mvn -DskipTests -Psparkr package
 ```
 
 #### Running sparkR

README.md

@@ -25,7 +25,7 @@ This README file only contains basic setup instructions.
 Spark is built using [Apache Maven](https://maven.apache.org/).
 To build Spark and its example programs, run:
 
-    build/mvn -DskipTests clean package
+    ./build/mvn -DskipTests clean package
 
 (You do not need to do this if you downloaded a pre-built package.)
 

docs/README.md

@@ -84,7 +84,7 @@ $ PRODUCTION=1 jekyll build
 
 ## API Docs (Scaladoc, Javadoc, Sphinx, roxygen2, MkDocs)
 
-You can build just the Spark scaladoc and javadoc by running `build/sbt unidoc` from the `$SPARK_HOME` directory.
+You can build just the Spark scaladoc and javadoc by running `./build/sbt unidoc` from the `$SPARK_HOME` directory.
 
 Similarly, you can build just the PySpark docs by running `make html` from the
 `$SPARK_HOME/python/docs` directory. Documentation is only generated for classes that are listed as
@@ -94,7 +94,7 @@ after [building Spark](https://github.com/apache/spark#building-spark) first.
 
 When you run `jekyll build` in the `docs` directory, it will also copy over the scaladoc and javadoc for the various
 Spark subprojects into the `docs` directory (and then also into the `_site` directory). We use a
-jekyll plugin to run `build/sbt unidoc` before building the site so if you haven't run it (recently) it
+jekyll plugin to run `./build/sbt unidoc` before building the site so if you haven't run it (recently) it
 may take some time as it generates all of the scaladoc and javadoc using [Unidoc](https://github.com/sbt/sbt-unidoc).
 The jekyll plugin also generates the PySpark docs using [Sphinx](http://sphinx-doc.org/), SparkR docs
 using [roxygen2](https://cran.r-project.org/web/packages/roxygen2/index.html) and SQL docs

docs/running-on-kubernetes.md

@@ -121,7 +121,7 @@ $ ./bin/docker-image-tool.sh -r <repo> -t my-tag -R ./kubernetes/dockerfiles/spa
 To launch Spark Pi in cluster mode,
 
 ```bash
-$ bin/spark-submit \
+$ ./bin/spark-submit \
     --master k8s://https://<k8s-apiserver-host>:<k8s-apiserver-port> \
     --deploy-mode cluster \
     --name spark-pi \

docs/running-on-mesos.md

@@ -212,7 +212,7 @@ protected (port 7077 by default).
 
 By setting the Mesos proxy config property (requires mesos version >= 1.4), `--conf spark.mesos.proxy.baseURL=http://localhost:5050` when launching the dispatcher, the mesos sandbox URI for each driver is added to the mesos dispatcher UI.
 
-If you like to run the `MesosClusterDispatcher` with Marathon, you need to run the `MesosClusterDispatcher` in the foreground (i.e: `bin/spark-class org.apache.spark.deploy.mesos.MesosClusterDispatcher`). Note that the `MesosClusterDispatcher` not yet supports multiple instances for HA.
+If you like to run the `MesosClusterDispatcher` with Marathon, you need to run the `MesosClusterDispatcher` in the foreground (i.e: `./bin/spark-class org.apache.spark.deploy.mesos.MesosClusterDispatcher`). Note that the `MesosClusterDispatcher` not yet supports multiple instances for HA.
 
 The `MesosClusterDispatcher` also supports writing recovery state into Zookeeper. This will allow the `MesosClusterDispatcher` to be able to recover all submitted and running containers on relaunch.   In order to enable this recovery mode, you can set SPARK_DAEMON_JAVA_OPTS in spark-env by configuring `spark.deploy.recoveryMode` and related spark.deploy.zookeeper.* configurations.
 For more information about these configurations please refer to the configurations [doc](configuration.html#deploy).
@@ -362,7 +362,7 @@ The External Shuffle Service to use is the Mesos Shuffle Service. It provides sh
 on top of the Shuffle Service since Mesos doesn't yet support notifying another framework's
 termination. To launch it, run `$SPARK_HOME/sbin/start-mesos-shuffle-service.sh` on all slave nodes, with `spark.shuffle.service.enabled` set to `true`.
 
-This can also be achieved through Marathon, using a unique host constraint, and the following command: `bin/spark-class org.apache.spark.deploy.mesos.MesosExternalShuffleService`.
+This can also be achieved through Marathon, using a unique host constraint, and the following command: `./bin/spark-class org.apache.spark.deploy.mesos.MesosExternalShuffleService`.
 
 # Configuration
 

docs/sql-data-sources-jdbc.md

@@ -36,7 +36,7 @@ spark classpath. For example, to connect to postgres from the Spark Shell you wo
 following command:
 
 {% highlight bash %}
-bin/spark-shell --driver-class-path postgresql-9.4.1207.jar --jars postgresql-9.4.1207.jar
+./bin/spark-shell --driver-class-path postgresql-9.4.1207.jar --jars postgresql-9.4.1207.jar
 {% endhighlight %}
 
 Tables from the remote database can be loaded as a DataFrame or Spark SQL temporary view using

docs/streaming-kinesis-integration.md

@@ -222,17 +222,17 @@ To run the example,
 	<div class="codetabs">
 	<div data-lang="scala" markdown="1">
 
-        bin/run-example --packages org.apache.spark:spark-streaming-kinesis-asl_{{site.SCALA_BINARY_VERSION}}:{{site.SPARK_VERSION_SHORT}} streaming.KinesisWordCountASL [Kinesis app name] [Kinesis stream name] [endpoint URL]
+        ./bin/run-example --packages org.apache.spark:spark-streaming-kinesis-asl_{{site.SCALA_BINARY_VERSION}}:{{site.SPARK_VERSION_SHORT}} streaming.KinesisWordCountASL [Kinesis app name] [Kinesis stream name] [endpoint URL]
 
 	</div>
 	<div data-lang="java" markdown="1">
 
-        bin/run-example --packages org.apache.spark:spark-streaming-kinesis-asl_{{site.SCALA_BINARY_VERSION}}:{{site.SPARK_VERSION_SHORT}} streaming.JavaKinesisWordCountASL [Kinesis app name] [Kinesis stream name] [endpoint URL]
+        ./bin/run-example --packages org.apache.spark:spark-streaming-kinesis-asl_{{site.SCALA_BINARY_VERSION}}:{{site.SPARK_VERSION_SHORT}} streaming.JavaKinesisWordCountASL [Kinesis app name] [Kinesis stream name] [endpoint URL]
 
 	</div>
 	<div data-lang="python" markdown="1">
 
-        bin/spark-submit --jars external/kinesis-asl/target/scala-*/\
+        ./bin/spark-submit --jars external/kinesis-asl/target/scala-*/\
             spark-streaming-kinesis-asl-assembly_*.jar \
             external/kinesis-asl/src/main/python/examples/streaming/kinesis_wordcount_asl.py \
             [Kinesis app name] [Kinesis stream name] [endpoint URL] [region name]
@@ -244,7 +244,7 @@ To run the example,
 
 - To generate random string data to put onto the Kinesis stream, in another terminal, run the associated Kinesis data producer.
 
-		bin/run-example streaming.KinesisWordProducerASL [Kinesis stream name] [endpoint URL] 1000 10
+		./bin/run-example streaming.KinesisWordProducerASL [Kinesis stream name] [endpoint URL] 1000 10
 
 	This will push 1000 lines per second of 10 random numbers per line to the Kinesis stream.  This data should then be received and processed by the running example.
 

resource-managers/kubernetes/integration-tests/README.md

@@ -11,7 +11,7 @@ is subject to change. Note that currently the integration tests only run with Ja
 The simplest way to run the integration tests is to install and run Minikube, then run the following from this
 directory:
 
-    dev/dev-run-integration-tests.sh
+    ./dev/dev-run-integration-tests.sh
 
 The minimum tested version of Minikube is 0.23.0. The kube-dns addon must be enabled. Minikube should
 run with a minimum of 4 CPUs and 6G of memory:
@@ -62,23 +62,23 @@ By default, the test framework will build new Docker images on every test execut
 and it is written to file at `target/imageTag.txt`. To reuse the images built in a previous run, or to use a Docker 
 image tag that you have built by other means already, pass the tag to the test script:
 
-    dev/dev-run-integration-tests.sh --image-tag <tag>
+    ./dev/dev-run-integration-tests.sh --image-tag <tag>
 
 where if you still want to use images that were built before by the test framework:
 
-    dev/dev-run-integration-tests.sh --image-tag $(cat target/imageTag.txt)
+    ./dev/dev-run-integration-tests.sh --image-tag $(cat target/imageTag.txt)
 
 ### Customising the Image Names
 
 If your image names do not follow the standard Spark naming convention - `spark`, `spark-py` and `spark-r` - then you can customise the names using several options.
 
 If you use the same basic pattern but a different prefix for the name e.g. `apache-spark` you can just set `--base-image-name <base-name>` e.g.
 
-    dev/dev-run-integration-tests.sh --base-image-name apache-spark
+    ./dev/dev-run-integration-tests.sh --base-image-name apache-spark
 
 Alternatively if you use completely custom names then you can set each individually via the `--jvm-image-name <name>`, `--python-image-name <name>` and `--r-image-name <name>` arguments e.g.
 
-    dev/dev-run-integration-tests.sh --jvm-image-name jvm-spark --python-image-name pyspark --r-image-name sparkr
+    ./dev/dev-run-integration-tests.sh --jvm-image-name jvm-spark --python-image-name pyspark --r-image-name sparkr
 
 ## Spark Distribution Under Test
 

sql/README.md

@@ -9,4 +9,4 @@ Spark SQL is broken up into four subprojects:
  - Hive Support (sql/hive) - Includes an extension of SQLContext called HiveContext that allows users to write queries using a subset of HiveQL and access data from a Hive Metastore using Hive SerDes. There are also wrappers that allow users to run queries that include Hive UDFs, UDAFs, and UDTFs.
  - HiveServer and CLI support (sql/hive-thriftserver) - Includes support for the SQL CLI (bin/spark-sql) and a HiveServer2 (for JDBC/ODBC) compatible server.
 
-Running `sql/create-docs.sh` generates SQL documentation for built-in functions under `sql/site`.
+Running `./sql/create-docs.sh` generates SQL documentation for built-in functions under `sql/site`.